Perceptual, reward-based, and other decisions are deliberative processes that depend on the ability to accumulate uncertain information over time. In dynamic environments, this process must be adaptive to account effectively for changes in the relevance and reliability of new inputs. For example, environmental changes can occur mid-decision. Such changes can render previous inputs obsolete and thus require adjustment of the accumulation process. Recent work has begun to examine decision-making under these kinds of dynamic conditions, resulting in a growing understanding of computational, behavioral, and physiological properties of adaptive evidence accumulation. However, a critical gap remains in our understanding of the underlying neural mechanisms: no study to date has identified representations of this kind of adaptive decision variable that flexibly accumulate information to drive behavior. Our goal is to fill this gap using highly interacting theoretical and experimental approaches to understand how and where in the brain such decision variables are encoded. Specifically, we will test the hypothesis that brain circuits that encode near-perfect integration of evidence under static conditions are highly flexible and implement more adaptive processes that approximate key features of ideal-observer models under dynamic conditions. We propose three Specific Aims, as follows. Aim 1 is to develop computational models of neural circuits that can approximate normative evidence accumulation in dynamic environments. Aim 2 is to determine principles of adaptive evidence accumulation used by human subjects performing dynamic decision tasks. Aim 3 is to identify-representations-of-adaptive-evidence-accumulation-in-parietaJ-and-prefrontaJ- ne1.Jra1-acti11ity-of monkeys performing dynamic decision tasks. Together, these integrated computational, behavioral, and neurophysiological approaches will provide novel insights into the many aspects of higher brain function and complex behaviors that depend on processing information in a manner that is not tied reflexively to immediate sensory inputs or motor outputs. We also have a strong data-sharing strategy that will help ensure that this unique data set will be made available for research and teaching purposes. 1 --- RELEVANCE (See instructions): The proposed work is basic research designed to identify mechanisms of flexible decision-making. In the long run, this work will help inform new diagnoses and treatments of disorders that include deficits in cognition and decision-making, including schizophrenia, autism, and attention-deficit/hyperactivity disorder (ADHD).